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Title: A semi-parametric density estimation Authors:  Seyed Mahdi Salehi - University of Neyshabur (Iran) [presenting]
Andriette Bekker - University of Pretoria (South Africa)
Mohammad Arashi - Ferdowsi University of Mashhad (Iran)
Abstract: A semi-parametric multivariate kernel density estimator is proposed with a more flexible family of kernels including skew-normal and skew-t. We show that the proposed estimator not only reduces boundary bias but also it is closer to the actual density compared to that of the usual estimator employing the Gaussian kernel. Finding optimum bandwidth under the mentioned asymmetric kernels is another main result where we shrink the bandwidth more than the one obtained under the normal assumption. Finally, through a numerical study, we will illustrate the application of the proposed semi-parametric kernel density estimator on density-based clustering using some simulated and real data sets.